Abstract
The main goal in our experimental study was to explore the impact of image compression on face detection using Haar-like features. In our setup we used the JPEG, JPEG2000 and JPEG XR compression standards to compress images from selected databases at given compression ratios. We performed the face detection using OpenCV on the reference images from the database as well as on the compressed images. After the detection process we compared the detected areas between the reference and the compressed image gaining the average coverage, false positive and false negative areas. Experimental results comparing JPEG, JPEG2000 and JPEG XR are showing that the average coverage of the detected face area differ between 79,58% in the worst and 99,61% in the best case. The false negative (not covered) areas range between 0,33% and 19,75% and false positive (fallout) areas between 0,38% and 9,45%. We conclude that the JPEG compression standard is performing worse than JPEG2000 and JPEG XR while both latter providing quite equal and good results.
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References
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, pp. 07–49. University of Massachusetts, Amherst (2007)
Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)
Frischholz, R.: Bao face database at the face detection homepage. http://www.facedetection.com (last accessed September 26, 2014)
Stump-based 20x20 gentle adaboost frontal face detector, Created by Rainer Lienhart. https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_alt.xml (last accessed September 23, 2014)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-511–I-518 (2001)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Horvath, K., Stögner, H., Uhl, A.: Effects of JPEG XR compression settings on iris recognition systems. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part II. LNCS, vol. 6855, pp. 73–80. Springer, Heidelberg (2011)
Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: Proceedings of the 2002 International Conference on Image Processing, vol. 1, pp. I-900–I-903 (2002)
Bradski, G.: The OpenCV Library. Dr. Dobb’s Journal of Software Tools (2000). http://drdobbs.com/opensource/184404319
Wallace, G.K.: The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics 38(1), 18–34 (1992)
Rakshit, S., Monro, D.M.: An Evaluation of Image Sampling and Compression for Human Iris Recognition. IEEE Transactions on Information Forensics and Security 2(3), 605–612 (2007)
Figueroa-Villanueva, M.A., Ratha, N.K., Bolle, R.M.: A comparative performance analysis of JPEG2000 vs. WSQ for fingerprint compression. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 385–392. Springer, Heidelberg (2003)
Kidd, R.C.: Comparison of wavelet scalar quantization and JPEG for fingerprint image compression. Journal of Electronic Imaging 4(1), 31–39 (1995)
Granai, L., Tena, J.R., Hamouz, M., Kittler, J.: Influence of compression on 3D face recognition. Pattern Recognition Letters, 30(8), 745–750
Delac, K., Grgic, S., Grgic, M.: Image compression in face recognition - a literature survey. In: Recent Advances in Face Recognition, pp. 236–250. I-Tech (2008)
Jeong, G.-M., Kim, C., Ahn, H.-S., Ahn, B.-J.: JPEG Quantization Table Design for Face Images and Its Application to Face Recognition. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science E69–A(11), 2990–2993 (2006)
Chen, M., Zhang, S., Karim, M.A.: Modification of standard image compression methods for correlation-based pattern recognition. Optical Engineering 43(8), 1723–1730 (2004)
Delac, K., Grgic, S., Grgic, M.: Face recognition in JPEG and JPEG2000 compressed domain. Image and Vision Computing 27, 1108–1120 (2009)
Kamasack, M., Sankur, B.: Face recognition under lossy compression. In: Proceedings of the International Conference on Pattern Recognition and Information Processing, PRIP 1999, pp. 27–32 (1999)
Klare, B., Burge, M.: Assessment of H.264 video compression on automated face recognition performance in surveillance and mobile video scenarios. In: Proceedings of SPIE, Biometric Technology for Human Identification VII, vol. 7667, p. 76670X (2010)
Korshunov, P., Ooi, W.T.: Video quality for face detection, recognition, and tracking. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 7(3), article 14 (2011)
Luo, H., Eleftheriadis, A.: On face detection in the compressed domain. In: Proceedings of the ACM International Conference on Multimedia, pp. 285–294 (2000)
Fonseca, P., Nesvadha, J.: Face detection in the compressed domain. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 2004, pp. 2015–2018 (2004)
Zhuang, S.-S., Lai, S.-H.: Face detection directly from h.264 compressed video with convolutional neural network. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 2009, pp. 2485–2488 (2009)
Quinn, G.W., Grother, P.J.: Performance of Face Recognition Algorithms on Compressed Images. NIST Interagency Report 7830, Information Technology Laboratory, The National Institute of Standards and Technology (2011)
Hämmerle-Uhl, J., Karnutsch, M., Uhl, A.: Evolutionary optimisation of JPEG2000 part 2 wavelet packet structures for polar iris image compression. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part I. LNCS, vol. 8258, pp. 391–398. Springer, Heidelberg (2013)
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Elmer, P., Lupp, A., Sprenger, S., Thaler, R., Uhl, A. (2015). Exploring Compression Impact on Face Detection Using Haar-like Features. In: Paulsen, R., Pedersen, K. (eds) Image Analysis. SCIA 2015. Lecture Notes in Computer Science(), vol 9127. Springer, Cham. https://doi.org/10.1007/978-3-319-19665-7_5
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